US7627494B2 - Systems, methods and computer program products for modeling a monetary measure for a good based upon technology maturity levels - Google Patents

Systems, methods and computer program products for modeling a monetary measure for a good based upon technology maturity levels Download PDF

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US7627494B2
US7627494B2 US10/453,395 US45339503A US7627494B2 US 7627494 B2 US7627494 B2 US 7627494B2 US 45339503 A US45339503 A US 45339503A US 7627494 B2 US7627494 B2 US 7627494B2
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distribution
monetary
maturity
measure
risk
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US20040249738A1 (en
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Scott H. Mathews
Vinay T. Datar
Kevin Feely
David J. Gauss
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Boeing Co
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Boeing Co
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Priority to US10/453,395 priority Critical patent/US7627494B2/en
Priority to PCT/US2004/017310 priority patent/WO2004109457A2/en
Priority to EP04754015.8A priority patent/EP1636668A4/de
Priority to JP2006515079A priority patent/JP5046642B2/ja
Priority to CNA2004800192701A priority patent/CN101410858A/zh
Publication of US20040249738A1 publication Critical patent/US20040249738A1/en
Priority to US12/603,215 priority patent/US8204775B2/en
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G06Q10/06375—Prediction of business process outcome or impact based on a proposed change
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/067—Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/03—Credit; Loans; Processing thereof
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/06—Asset management; Financial planning or analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance

Definitions

  • the present invention generally relates to systems, methods and computer program products for modeling cost and associated revenue associated with a project for the manufacture and sale of a good and, more particularly, relates to systems, methods and computer program products for modeling cost and associated revenue associated with a project based upon at least one technology maturity level.
  • development of the project can include one or more different technologies, with different technologies in different stages of development.
  • a project can include a technology associated with materials utilized to manufacture the good that is in one stage of development, and a technology associated a particular manufacturing process that is utilized during the production of the good that is in another stage of development.
  • TRL's Technology Readiness Levels
  • NAA National Aeronautics and Space Administration
  • TRL 1 the levels of technology increase through TRL 9, which corresponds to actual successful use of a fully developed project in an applicable environment for the project.
  • TRL 9 the point of troubleshooting problems discovered during testing, which typically occur at TRL 8.
  • the state of development of the technologies associated with a project can have a direct impact on the uncertainty, and as such the risk, associated with the cost and/or revenues of manufacturing and selling the good.
  • the ability to correctly estimate the technical risk impact on a project enables manufacturers to make better decisions as to investment and proportion of the investment to the level of project risk.
  • manufacturers have not had the ability to reliably quantify the technical risk of a project proposal that has limited information, as extensive uncertainties, is concerned with new markets or technologies, or does not yet have a well-defined scope or specifications.
  • the present invention provides systems, methods and computer program products for modeling a monetary measure of a good, such as a cost and/or revenue associated with a good, where the model is based upon at least one, and more typically a plurality, of technology maturity levels.
  • the systems, methods and computer program products of embodiments of the present invention are capable of modeling quantitative risk/return as a function of qualitative measures of maturity to thereby reliably quantify the technical risk of a good based upon the level of maturity of the technologies associated with the good.
  • embodiments of the present invention are capable of modeling the quantitative risk/return for technologies at a number of different levels of development, where each level of development has an associated technical risk, or uncertainty.
  • embodiments of the present invention are capable of modeling quantitative risk/return in a robust manner to model monetary measures in a number of different contexts, without modifying the model to fit a particular monetary measure, good, project or the like.
  • Embodiments of the present invention are thus capable of easily assigning accurate quantitative assessments of risk and return through the extraction of distribution statistical parameters characteristic of representative capital and industry markets.
  • embodiments of the present invention are capable of enabling accurate, rapid and standardized characterization of cost and revenue uncertainties, and enabling rapid prototyping of business case simulations that apply techniques such as the Monte Carlo techniques.
  • systems, methods and computer program products are provided for modeling quantitative risk/return as a function of a plurality of qualitative measures of maturity, such as technology readiness levels.
  • the method of one embodiment begins by determining a risk/return distribution for each of a plurality of risk values, where the risk values can be ordered from a lowest risk value to a highest risk value.
  • a qualitative measure of maturity is assigned to each of the plurality of risk/return distributions based upon a respective risk value to thereby model quantitative risk/return as a function of the plurality of measures of maturity.
  • the measures of maturity can be ordered from a highest measure of maturity to a lowest measure of maturity.
  • the measures of maturity can be assigned by assigning a higher measure of maturity to a risk/return distribution for a lower risk value.
  • a risk/return distribution can be determined for each of a plurality of risk values based upon a log mean and a log standard deviation of a lognormal probability density reflecting a respective risk and return pattern of capital markets, such as according to the Capital Asset Pricing Model (CAPM).
  • each risk/return distribution typically comprises a lognormal distribution.
  • each of the risk/return distributions can be normalized to a mode of one. Additionally, or alternatively, an upper and lower bound of each of the risk/return distributions can be truncated by a predefined percentage.
  • each lognormal risk/return distribution can be converted to a corresponding triangular distribution.
  • assigning a qualitative measure can include assigning a qualitative measure of maturity to each triangular distribution.
  • a systems, methods and computer program products are provided for modeling a monetary measure of a good, such as a cost or revenue associated with the good.
  • the method of one embodiment begins by selecting at least one qualitative measure of maturity for at least one technology associated with the good.
  • each qualitative measure of maturity is associated with a distribution such that each technology is associated with the distribution of the respective qualitative measure of maturity.
  • the qualitative measures of maturity can be selected from a model of quantitative risk/return, as such may be determined as described above.
  • a monetary point such as a most likely cost or revenue value
  • a monetary distribution is then determined for each technology based upon the respective monetary point and a respective distribution.
  • a plurality of monetary values are selected by randomly selecting, such as according to the Monte Carlo technique, the plurality of monetary values for each technology based upon the respective monetary distribution.
  • the monetary measure for the good is modeled based upon the selected monetary values for each technology.
  • embodiments of the present invention are capable of modeling a monetary measure of a good, such as a cost and/or revenue associated with a good, based upon technology maturity levels.
  • embodiments of the present invention can model quantitative risk/return as a function of qualitative measures of maturity to thereby reliably quantify the technical risk of a good based upon the level of maturity of the technologies associated with the good.
  • embodiments of the present invention are capable of modeling the quantitative risk/return for technologies at a number of different levels of development, where each level of development has an associated technical risk, or uncertainty.
  • embodiments of the present invention are capable of modeling quantitative risk/return in a robust manner such that monetary measures can be modeled in a number of different contexts, without modifying the model to fit a particular monetary measure, good, project or the like.
  • FIG. 1 is a flow chart illustrating various steps in a method of modeling risk/return as a function of measures of maturity according to one embodiment of the present invention
  • FIG. 2 is an illustration of a risk/return distribution and a corresponding triangular distribution according to one embodiment of the present invention
  • FIG. 3 is a flow chart illustrating various steps in a method of modeling a monetary measure of a good according to one embodiment of the present invention.
  • FIG. 4 is a schematic block diagram of the system of one embodiment of the present invention embodied by a computer.
  • the method of this embodiment can begin by modeling returns as a function of risk, or uncertainty, associated with bringing a good to market. Returns can be modeled in any of a number of different manners, such as by estimators or the like. In one embodiment, for example, the returns are modeled from two return values and associated risk values, as such may be determined by an estimator. Then, assuming an approximately linear relationship between risk and return, the returns can be modeled as a linear function of risk based upon the two return values and associated risk values.
  • two return values may comprise 10.0% and 12.5%, with associated risk values comprising 20% and 30%, respectively.
  • the return model can be utilized to model future revenues of a good, where the future revenues are subject to an amount of uncertainty.
  • risk/return distributions are determined for different risk values, as shown in block 12 .
  • the risk/return distributions can be determined for any number of different risk values but, in one embodiment, the number of different risk values corresponds to the number of different qualitative measures of maturity, as described below. In this regard, the number of different qualitative measures of maturity are typically predefined. For example, when the number of qualitative measures of maturity equals nine, the number of different risk, or uncertainty, values can comprise those shown in Table 1, along with risk/return distribution can also be defined by a percentage standard deviation of the lognormal probability density reflecting a risk and return pattern of capital markets.
  • the risk/return distributions can be robustly applied to any of a number of different monetary measures without having to be modified to fit a particular monetary measure, good, project or the like.
  • each risk/return distribution can be normalized to a mode of one, as illustrated in block 14 . It will be appreciated by those skilled in the art that at the highest bounds of possible risk/return, goods are typically dropped or re-scoped into different goods. Similarly,
  • the risk/return distributions can be determined to define the entire range of possible risk/return, in one embodiment the lowest and highest bounds of each risk/return distribution are truncated, as shown in block 16 .
  • the each bound can be truncated by a predefined percentage (e.g., 2.5%) so that the risk/return distributions define the rage of most likely risk/return.
  • the risk/return distributions can be summarized for a number of different risk values, as shown below in Table 2.
  • Table 2 includes the mean value for each risk/return distribution.
  • the mean value cannot be directly determined but, instead, may be determined as that value that uniquely satisfies a lognormal distribution that has two constraints: (1) a standard deviation of X %, and (2) a mode value equal to one. More particularly, as can be seen from the previous equations, if the standard deviation is known, but the mean value is not known (as are the lognormal mean and lognormal standard deviation values), solving for the mean value is circular. As such, the only way to solve for the mean value is by utilizing a search algorithm with constraints.
  • the risk/return distributions can, but need not, be converted to roughly equivalent triangular distributions, as shown in block 18 .
  • the risk/return distributions can be converted to triangular distributions without sacrificing much accuracy during the conversion.
  • FIG. 2 For an example of a risk/return distribution and a corresponding triangular distribution, see FIG. 2 . It should be understood, however, that the risk/return distributions need not be converted to triangular distributions to practice the present invention.
  • the risk/return distributions can be converted to triangular distributions according to any of a number of different techniques. According to one technique, for
  • the triangular distribution can be normalized to a mode equal to one. Therefore, continuing the example of Table 2 above, the triangular distributions associated with respective risk/return distributions of Table 2 can be summarized as in Table 3, below.
  • Table 3 the triangular distributions associated with respective risk/return distributions of Table 2 can be summarized as in Table 3, below.
  • a point, or most likely, cost or revenue can be mapped onto a respective triangular distribution that has not been normalized.
  • the respective triangular distribution can have a mode not equal to one, which can then be accounted for when mapping the most likely cost or revenue onto the respective triangular distribution, as such is known to those skilled in the art.
  • the triangular distributions can be assigned to different qualitative measures of maturity to thereby model risk/return as a function of measures of maturity, as shown in block 20 .
  • the qualitative measures of maturity can include any number of different measures that typically describe a number of levels of maturity of a technology from inception through to fully developed. The number of measures of maturity can vary as desired, with each level describing a different level of maturity. Whereas the triangular distributions can be assigned to any of a number of different
  • each percentage standard deviation of the risk/return distributions is associated with an equal percentage uncertainty, or risk.
  • the percentage standard deviation of 20% from Table 2 above can be associated with a 20% uncertainty, as such is shown in Table 1.
  • the percentage standard deviation of 30% from Table 2 can be associated with a 30% uncertainty, as such is shown in Table 1, and so forth.
  • Each uncertainty in turn, can be associated with a different measure of maturity. As will be appreciated, typically the more mature a technology, the lower the risk. Therefore, each uncertainty can be associated with a measure of maturity, with the lower levels of uncertainty associated with the higher measures of maturity describing higher levels of development.
  • level 1 corresponds to the lowest level of development
  • levels 2 - 9 corresponding to increasing levels of development.
  • level 1 can be associated with the largest uncertainty, that is, an uncertainty of 100%
  • level 9 can be associated with the smallest uncertainty, that is, an uncertainty of 20%, and so forth.
  • each measure of maturity can be associated with a risk/return distribution, and therefore, a triangular distribution.
  • the measures of maturity can be associated with the triangular distributions in a manner summarized in Table 4, below.
  • each uncertainty is associated with a NASA TRL, as well as with a return value, as such may be determined as described above.
  • each NASA TRL is associated with a triangular distribution, defined by a minimum, a maximum and a mode, with a max/min and mean also shown.
  • each percentage of uncertainty is associated with a triangular distribution having a maximum and a minimum equal to the truncated bounds of a risk/return distribution having a percentage standard deviation equal to the percentage of uncertainty.
  • 20% uncertainty is associated with a triangular distribution having a maximum and a minimum of 1.50 and 0.72, respectively, which equal the 2.5% and 97.5% bounds of a risk/return distribution having a 20% standard deviation.
  • FIG. 4 illustrates one embodiment of a method of determining a monetary distribution, such as a cost or revenue distribution, associated with a good.
  • the method begins by assigning a measure of maturity, and therefore a triangular distribution, to the technology, as such may be assigned by a technologist or the like.
  • a point cost (recurring or nonrecurring) or revenue value, or more typically a most likely cost or revenue value, associated with the technology is defined or otherwise provided, such as by an estimator or the like.
  • the most likely cost or revenue value will thereafter be designated as the mode of a cost or revenue distribution.
  • the cost or revenue distribution can be determined by determining the minimum and maximum values, or more typically the truncated minimum and maximum values, that define the cost or revenue distribution.
  • the minimum and maximum values can be determined by simply multiplying the most likely cost or revenue by the minimum and maximum values of the respective triangular distribution. Thereafter, the cost or revenue distribution can be defined by the minimum and maximum, as well as the most likely cost or revenue.
  • a cost or revenue can be selected that accounts for a qualitative measure of maturity of the respective technology and, as such, the risks and returns associated with the respective technology, as shown in block 28 .
  • the cost or revenue can be selected in any of a number of different manners.
  • the cost or revenue value can be selected according to a method for randomly selecting the cost or revenue value, such as the Monte Carlo technique.
  • the Monte Carlo technique is a method of randomly generating values for uncertain variables to simulate a model.
  • a number of other cost or revenue values can be selected, such as according to the Monte Carlo technique, as shown in blocks 28 and 30 .
  • a sensitivity distribution can be determined based upon the values, as shown in block 32 .
  • a sensitivity distribution can be determined by determining a mean and a standard deviation from the selected cost or revenue values, and thereafter defining the sensitivity distribution based upon the mean and standard deviation.
  • the sensitivity distribution can be defined to comprise any of a number of different types of distributions, but in one embodiment, the sensitivity distribution comprises a lognormal distribution.
  • a cost sensitivity distribution can be determined from the cost values.
  • a price sensitivity distribution can be determined from the revenue values.
  • the cost sensitivity distribution generally assigns a probability of producing one unit of the good to each respective recurring cost at which manufacturers would produce the good.
  • the price sensitivity distribution generally assigns a probability of a unit purchase of the good to each respective price at which consumers would purchase the unit. The price sensitivity distribution and/or the cost sensitivity distribution can then be used, such as to model demand and associated profitability of a good, such as is described in U.S.
  • Contingent claims oftentimes come in the form of a call in which the manufacturer has an option to invest an amount of money, or additional amounts of money, in order to start producing or continue producing the good.
  • the manufacturer will likely decline to invest the money, or additional money, and thereby forego exercise of the call and will therefore decline to produce the good or terminate production of the good.
  • the manufacturer will likely make the necessary investment in order to begin or continue production of the good.
  • the manufacturer can avoid overpaying for production of the good as a result of an overvaluation of the contingent claim.
  • the manufacturer can identify goods in which the value of the contingent claim has been undervalued and can give strong consideration to investing in the production of these goods since they likely represent worthwhile investment opportunities.
  • the cost and revenue risks such as in determining the cost and price sensitivity distributions, the demand and cost of a good and, thus, the profitability of a good, can be accurately modeled.
  • the systems, methods and computer program products of the present invention can facilitate determining the value of the good and, in particular, the contingent claim at the present time.
  • the systems, methods and computer program products of the present invention can facilitate determining the value of the good and, in particular, the contingent claim at the present time.
  • a cost or revenue distribution can be determined for each such technology associated with the good. For example, see Table 5, which illustrates a good having a total of twelve technologies utilized to bring the good to market, with each technology having an associated measure of maturity and a most likely recurring cost value. Then, from the triangular distributions associated with the measures of maturity and the most likely recurring cost value, a cost or revenue distribution, such as a recurring cost distribution, can be determined for each technology. In addition, a total cost or revenue distribution can be associated with the good based upon the sum of the most likely cost or revenue values of the individual technologies, as well as the sum of the minimum and maximum values, respectively, of the individual technologies.
  • a cost or revenue when the good is associated with a plurality of technologies, a cost or revenue can be selected for each technology based upon the respective cost or revenue distribution, such as according to the Monte Carlo technique. Thereafter, the individual selected costs or revenues can be summed into a total cost or revenue for the good. Alternatively, a cost or revenue for the good can be selected, such as according to the Monte Carlo technique, based upon the total cost or revenue distribution, as such may be determined as described above. Like before, after selecting the cost or revenue value, a number of other cost or revenue values can be selected, such as according to the Monte Carlo technique. Then, a sensitivity distribution, such as a cost sensitivity or price distribution, can be determined based upon the values.
  • embodiments of the present invention are capable of modeling a monetary measure of a good, such as a cost and/or revenue associated with a good, based upon technology maturity levels.
  • embodiments of the present invention can model quantitative risk/return as a function of qualitative measures of maturity to thereby reliably quantify the technical risk of a good based upon the level of maturity of the technologies associated with the good.
  • embodiments of the present invention are capable of modeling the quantitative risk/return for technologies at a number of different levels of development, where each level of development has an associated technical risk, or uncertainty.
  • embodiments of the present invention are capable of modeling quantitative risk/return in a robust manner such that monetary measures can be modeled in a number of different contexts, without modifying the model to fit a particular monetary measure, good, project or the like.
  • the system of the present invention is typically embodied by a processing element and an associated memory device, both of which are commonly comprised by a computer 40 or the like.
  • the method of embodiments of the present invention can be performed by the processing element manipulating data stored by the memory device with any one of a number of commercially available computer software programs.
  • the method can be performed with data that is capable of being manipulated and/or presented in spreadsheet form.
  • the method can be performed by the processing element manipulating data stored by the memory device with Excel, a spreadsheet software program distributed by the Microsoft Corporation of Redmond, Wash., including Crystal Ball, a Monte Carlo simulation software program distributed by Decisioneering, Inc. of Denver, Colo.
  • the computer can include a display 42 for presenting information relative to performing embodiments of the method of the present invention, including the various distributions, models and/or conclusions as determined according to embodiments of the present invention.
  • the computer can further include a printer 44 .
  • the computer 40 can include a means for locally or remotely transferring the information relative to performing embodiments of the method of the present invention.
  • the computer can include a facsimile machine 46 for transmitting information to other facsimile machines, computers or the like.
  • the computer can include a modem 48 to transfer information to other computers or the like.
  • the computer can include an interface (not shown) to a network, such as a local area network (LAN), and/or a wide area network (WAN).
  • LAN local area network
  • WAN wide area network
  • the computer can include an Ethernet Personal Computer Memory Card International Association (PCMCIA) card configured to transmit and receive information to and from a LAN, WAN or the like.
  • PCMCIA Personal Computer Memory Card International Association
  • the methods according to embodiments of the present invention may be embodied in a software or data module, component, portfolio or the like, that can be manipulated or otherwise operated within a spreadsheet software program such as Excel.
  • a technique may be advantageous in a number of different contexts, such as in the context of financial modeling and analysis.
  • modules, components and/or a portfolio that perform various financial modeling functions can be combined to gain a more complete understanding of a financial context.
  • data capable of being manipulated to perform at least a portion of the methods of the present invention can be embodied in a module, which can thereafter be linked or otherwise associated with other portions of the methods of the present invention embodied in other modules so as to formulate a component. Then, if so desired, the component can be linked or otherwise associated with other components capable of performing other related methods to thereby form a portfolio.
  • methods of determining the price sensitivity distribution according to embodiments of the present invention can be embodied in one module. The module can then be linked or otherwise associated with another module for modeling demand such that the demand module can model demand based upon the price sensitivity distribution determined by the price sensitivity module.
  • the demand module can then be linked or otherwise associated with a module for modeling cost to thereby formulate a component capable of modeling profitability based upon the demand and cost models. Then, if so desired, the component for modeling profitability can be linked or otherwise associated with another component to perform another function.
  • the component for modeling profitability can be linked or otherwise associated with a component capable of forecasting revenue over time to thereby create a business case for the good.
  • such a component capable of forecasting revenue over time may operate according to U.S.
  • the system of the present invention generally operates under control of a computer program product according to another aspect of the present invention.
  • the computer program product for performing the methods of embodiments of the present invention includes a computer-readable storage medium, such as the non-volatile storage medium, and computer-readable program code portions, such as a series of computer instructions, embodied in the computer-readable storage medium.
  • FIGS. 1 and 3 are a flowchart of methods, systems and program products according to the invention. It will be understood that each block or step of the flowchart, and combinations of blocks in the flowchart, can be implemented by computer program instructions. These computer program instructions may be loaded onto a computer or other programmable apparatus to produce a machine, such that the instructions which execute on the computer or other programmable apparatus create means for implementing the functions specified in the flowchart block(s) or step(s).
  • These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart block(s) or step(s).
  • the computer program instructions may also be loaded onto a computer or other programmable apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block(s) or step(s).
  • blocks or steps of the flowchart support combinations of means for performing the specified functions, combinations of steps for performing the specified functions and program instruction means for performing the specified functions. It will also be understood that each block or step of the flowchart, and combinations of blocks or steps in the flowchart, can be implemented by special purpose hardware-based computer systems which perform the specified functions or steps, or combinations of special purpose hardware and computer instructions.

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